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MIL OSI Translation. Region: Russian Federation –

Source: Novosibirsk State University – Novosibirsk State University –

On January 8 of this year, the first quartile journal of the Wiley publishing house “Microscopy Research and Technique” published the article “DLgram cloud service for in-depth analysis of microscopic images” (“DLgram cloud service for deep-learning analysis of microscopy images”). In it, scientists from the laboratory of deep machine learning in physical methods AndInstitute of Intelligent Robotics NSU presented a universal service they developed for recognizing numerous homogeneous objects of various types. Their work was supported by the Russian Science Foundation grant No. 22-23-00951.

– We presented the first version of this service back in 2021. Then, thanks to the support of a grant from the Russian Science Foundation, they improved it: they expanded the functionality and introduced new capabilities for analyzing recognized objects. Now our telegram service is quite famous among microscopists and has more than 300 users from Russia and abroad. No special skills are required to work with it. A neural network frees a person from performing routine processes associated with counting and characterizing particles or objects in an image – determining their quantity and concentration, as well as maximum, minimum and average sizes. The service operates in cloud mode, i.e. The Cascade Mask-RCNN neural network used does not work on the user’s computer, but on the graphic server of the Institute of Intelligent Robotics of NSU,” said Andrey Matveev, head of the laboratories.

– Any user can work with the DLgram service; no special knowledge or skills are required. It is enough to mark about 10 objects on your image in a selected square and upload it to the Nanoparticles telegram channel (http://t.te/nanopartiytsles_nsk). After this, the image is picked up by the chatbot and goes to the server of the Institute of Intelligent Robotics, where the neural network is trained, and then objects in the image are recognized. This process only takes a few minutes. The user receives an image with recognized objects and can make adjustments if necessary. After this, the parameters of the objects are determined – quantity, size, area, concentration,” explained senior researcher at the laboratory Anna Nartova.

The new service works with various photographic images – pictures from microscopes, cameras and mobile phones, and the neural network recognizes not only particles of substances, but also macro-objects.

– We are actively developing the creation of such digital assistants not only in the field of microscopy. Employees and students of the institute are working on creating automatic recognition systems for various objects in industry, as well as for the urban environment, as part of the federal project for the development of Artificial Intelligence Centers, said Alexey Okunev, director of the Institute of Intelligent Robotics at NSU.

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Please note; This information is raw content directly from the information source. It is accurate to what the source is stating and does not reflect the position of MIL-OSI or its clients.

EDITOR’S NOTE: This article is a translation. Apologies should the grammar and or sentence structure not be perfect.

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